In today’s competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, Talent and Management Computing have increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop is to bring together researchers and practitioners to discuss both the critical problems faced by talent and management related domains, and potential data-driven solutions by leveraging state-of-the-art data mining technologies.


Program Sketch (Tentative)

13:00-13:20     Opening & Welcome

13:20-13:55     Paper presentation 1:   Acqui-hiring or Acqui-quitting: Data-driven Post-M&A Turnover Prediction via a Dual-fit GNN Model, Speaker: Denghui Zhang

13:55-14:30     Paper presentation 2:   MANE: Organizational Network Embedding with Multiplex Attentive Neural Networks, Speaker: Yuyang Ye

14:30-15:05     Paper presentation 3:   Demand-Supply Joint Labor Market Analysis, Speaker: Hao Liu

15:05-15:35     Coffee Break

15:35-16:10     Paper presentation 4:   AI-Enhanced Customizable Career Path Planning: A Long-Term Perspective, Speaker: Keli Xiao

16:10-16:45     Paper presentation 5:   Empirical Evaluation of Text-Driven Job Salary Prediction, Speaker: Nikhil Narayane

16:45-17:00     Closing Remarks


This workshop aims to bring together leading researchers and practitioners to exchange and share their experiences and latest research/application results on all aspects of Talent and Management Computing based on data mining technologies. It will provide a premier interdisciplinary forum to discuss the most recent trends, innovations, applications as well as the real-world challenges encountered and corresponding data-driven solutions in relevant domains.

The topics of interest include but not limited to:

  • Talent behavior modeling
  • Talent personality and leadership
  • Talent performance assessment
  • Talent recruitment
  • Talent retention and incentive
  • Job recommendation
  • Person-job fit and job satisfaction
  • Career development
  • Career path modeling
  • Professional social networks
  • Team formation and task assignment
  • Group-based decision-making
  • Organizational change and stability
  • Organizational culture and communication
  • Organizational competition analysis
  • Labour market intelligence
  • Strategic management and planning
  • Fairness in talent and management computing


We invite the submission of regular research papers (9 pages), as well as vision papers and short technical papers (around 4-6 pages), including all content and references. Submissions must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template.

To encourage the discussion, both original papers, and papers which have been published before, are all welcome to be submitted to this workshop. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Considering the practical characters of this workshop, to enrich the presentations, we strongly encourage the authors to submit their demonstrations, e.g., intelligent system for talent analytics, which will also be evaluated during the review process.

All the papers are required to be submitted via the EasyChair system.

Important Dates

June 4, 2023: Workshop paper submission due (23:59, Anywhere on Earth)

June 23, 2023: Workshop paper notifications

August 6, 2023: Workshop Day


Hengshu Zhu

Career Science Lab (CSL), BOSS Zhipin

Yong Ge

The University of Arizona

Hui Xiong

The Hong Kong University of Science and Technology (Guangzhou)

Ee-Peng Lim

Singapore Management University